Retaining a set of accountholders within a ceiling number radius

ABSTRACT

Systems and methods of improving the operation of a transaction network and transaction network devices is disclosed. A lightning KNN host may comprise various modules and engines as discussed herein wherein lookalike records may be identified whereby the speed of the lightning KNN network may be enhanced and the accuracy and precision of results improved whereby the transaction network more properly functions according to approved parameters.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of, claims priority to and thebenefit of, U.S. Ser. No. 15/018,393 filed Feb. 8, 2016 and entitled“SYSTEM AND METHOD FOR DATA ANALYTICS,” which is incorporated byreference herein in its entirety for all purposes.

FIELD

The present disclosure relates to data analytics for transaction data.

BACKGROUND

Large data sets may exist in various sizes and levels of organization.With big data comprising data sets as large as ever, the volume of datacollected incident to the increased popularity of online and electronictransactions continues to grow. Billions of rows and hundreds ofthousands of columns worth of data may populate a single table, forexample. An example of the use of big data is in identifying andcategorizing business spending and consumer spending, which isfrequently a key priority for transaction card issuers. However,transactions processed by the transaction card issuer are massive involume and comprise tremendously large data sets. Companies frequentlydesire to process and analyze this data; however, such processing andanalysis is typically time consuming and resource intensive due to thevolume of data. These limitations confuse and frustrate theidentification and categorization of transaction data, while alsohampering data analytics.

SUMMARY

In accordance with various embodiments, a lightning KNN (known nearestneighbor) host may include a historical data retrieval engine configuredto load historically processed data, a distance evaluator configured toevaluate a distance between a cluster value of each set and acorresponding value of a field of a record of a new cardholder, wherebya plurality of new-cardholder-to-cluster-value distances are determined,and an outer radius boundary determiner is configured to determine anouter radius boundary of each set. The lightning KNN host may include afirst ceiling number radius receiver configured to receive a firstceiling number radius, a set discarder configured to retain each setwith a new-cardholder-to-cluster-value distance locating a portion ofthe set within the first ceiling number radius, and a communication busdisposed in logical communication with the historical data retrievalengine, the distance evaluator, the outer radius boundary determiner,and the first ceiling number radius receiver, and. or the set discarder.The lightning KNN host may include a bus controller disposed in logicalcommunication with the communication bus and configured to directcommunication among the historical data retrieval engine, the distanceevaluator, the outer radius boundary determiner, the first ceilingnumber radius receiver, and/or the set discarder. The record may bemapped according to a lightning KNN method.

The lightning KNN host may include a field value determiner configuredto determine a value of the field of the record of a plurality ofrecords, a record grouper configured to group the plurality of recordsinto sets, and a historical data storage engine configured to store eachset and an associated cluster proximity and an associated cluster valuein a historically processed data set.

In various embodiments, each record of the plurality of recordsrepresents a cardholder. In various embodiments, the cluster valueincludes a center value of the set. In various embodiments, the field ofthe record of the new cardholder includes a credit limit. In variousembodiments, the new-cardholder-to-cluster-value distance includes thedistance between the cluster value and a value of the field of therecord of the new cardholder. In various embodiments, the outer radiusboundary of the set includes a sum of a cluster proximity and thenew-cardholder-to-cluster-value distance.

A lightning KNN method may include loading the historically processeddata from a node of a distributed storage system, evaluating a distancebetween a cluster value of each set and a corresponding value of a fieldof a record including a new cardholder, whereby a plurality ofnew-cardholder-to-cluster-value distances are determined, and adding acluster proximity of each set to a new-cardholder-to-cluster-valuedistance of the set to form an outer radius boundary of the setincluding a distance from the new cardholder to a farthest most boundaryof each set. The method may include receiving a first ceiling numberradius including a distance from between the new cardholder encircling afirst ceiling number of records, and discarding each set with thenew-cardholder-to-cluster-value distance not locating a portion of theset within the first ceiling number radius.

In various embodiments, the method may include determining a value ofthe field of the record for all records representing a cardholder,grouping the records into sets located within the cluster proximity ofthe cluster value, wherein the cluster value includes a center point ofthe set and the cluster proximity includes a radius about the clustervalue, and storing the set and associated cluster proximity and thecluster value in a historically processed data database.

In various embodiments, each record represents a cardholder. In variousembodiments, the cluster value includes a center value of the set. Invarious embodiments, the field of the record of the new cardholderincludes a credit limit. In various embodiments, thenew-cardholder-to-cluster-value distance includes the distance betweenthe cluster value and a value of the field of the record of the newcardholder. In various embodiments, the outer radius boundary of the setincludes a sum of the cluster proximity and thenew-cardholder-to-cluster-value distance.

A lightning KNN network may include a lightning KNN host configured tomap a cardholder according to a lightning KNN method, wherein thelightning KNN host directs data to be stored, a distributed storagesystem including a plurality of nodes, and the distributed storagesystem configured to direct data to the lightning KNN host, in responseto the lightning KNN method of the lightning KNN host. The network mayinclude a telecommunications transfer channel including a networklogically connecting the lightning KNN host to the distributed storagesystem.

The forgoing features and elements may be combined in variouscombinations without exclusivity, unless expressly indicated hereinotherwise. These features and elements as well as the operation of thedisclosed embodiments will become more apparent in light of thefollowing description and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter of the present disclosure is particularly pointed outand distinctly claimed in the concluding portion of the specification. Amore complete understanding of the present disclosure, however, may beobtained by referring to the detailed description and claims whenconsidered in connection with the drawing figures, wherein like numeralsdenote like elements.

FIG. 1A illustrates an exemplary system for distributed storage anddistributed processing, in accordance with various embodiments;

FIG. 1B illustrates an exemplary lightning KNN host component of asystem according to FIG. 1A, in accordance with various embodiments;

FIG. 2 illustrates an exemplary lightning KNN host component I/Oscenario of a lightning KNN host component according to FIG. 1B, inaccordance with various embodiments;

FIG. 3 illustrates an exemplary lightning KNN method, in accordance withvarious embodiments.

FIG. 4A-F illustrates various aspects of an exemplary lightning KNNmethod of FIG. 3, in accordance with various embodiments.

DETAILED DESCRIPTION

The detailed description of various embodiments herein makes referenceto the accompanying drawings and pictures, which show variousembodiments by way of illustration. While these various embodiments aredescribed in sufficient detail to enable those skilled in the art topractice the disclosure, it should be understood that other embodimentsmay be realized and that logical and mechanical changes may be madewithout departing from the spirit and scope of the disclosure. Thus, thedetailed description herein is presented for purposes of illustrationonly and not of limitation. For example, the steps recited in any of themethod or process descriptions may be executed in any order and are notlimited to the order presented. Moreover, any of the functions or stepsmay be outsourced to or performed by one or more third parties.Furthermore, any reference to singular includes plural embodiments, andany reference to more than one component may include a singularembodiment.

With reference to FIG. 1A, system 100 for distributed data storage andprocessing is shown, in accordance with various embodiments. System 100may comprise a lightning KNN host 102. Lightning KNN host 102 maycomprise any device capable of receiving and/or processing an electronicmessage via telecommunications transfer channel 104. Telecommunicationstransfer channel 104 may comprise a network. Lightning KNN host 102 maytake the form of a computer or processor, or a set ofcomputers/processors, although other types of computing units or systemsmay be used, including laptops, notebooks, hand held computers, personaldigital assistants, cellular phones, smart phones (e.g., iPhone®,BlackBerry®, Android®, etc.) tablets, wearables (e.g., smart watches andsmart glasses), or any other device capable of receiving data overtelecommunications transfer channel 104.

As used herein, the term “network” includes any cloud, cloud computingsystem or electronic communications system or method which incorporateshardware and/or software components. Communication among the parties maybe accomplished through any suitable communication channels, such as,for example, a telephone network, an extranet, an intranet, Internet,point of interaction device (point of sale device, personal digitalassistant (e.g., iPhone®, Blackberry®), cellular phone, kiosk, etc.),online communications, satellite communications, off-linecommunications, wireless communications, transponder communications,local area network (LAN), wide area network (WAN), virtual privatenetwork (VPN), networked or linked devices, keyboard, mouse and/or anysuitable communication or data input modality. Moreover, although thesystem is frequently described herein as being implemented with TCP/IPcommunications protocols, the system may also be implemented using IPX,Appletalk, IP-6, NetBIOS, OSI, any tunneling protocol (e.g. IPsec, SSH),or any number of existing or future protocols. If the network is in thenature of a public network, such as the Internet, it may be advantageousto presume the network to be insecure and open to eavesdroppers.Specific information related to the protocols, standards, andapplication software utilized in connection with the Internet isgenerally known to those skilled in the art and, as such, need not bedetailed herein. See, for example, DILIP NAIK, INTERNET STANDARDS ANDPROTOCOLS (1998); JAVA 2 COMPLETE, various authors, (Sybex 1999);DEBORAH RAY AND ERIC RAY, MASTERING HTML 4.0 (1997); and LOSHIN, TCP/IPCLEARLY EXPLAINED (1997) and DAVID GOURLEY AND BRIAN TOTTY, HTTP, THEDEFINITIVE GUIDE (2002), the contents of which are hereby incorporatedby reference.

A network may be unsecure. Thus, communication over the network mayutilize data encryption. Encryption may be performed by way of any ofthe techniques now available in the art or which may becomeavailable—e.g., Twofish, RSA, El Gamal, Schorr signature, DSA, PGP, PM,GPG (GnuPG), and symmetric and asymmetric cryptography systems.

In various embodiments, lightning KNN host 102 may interact withdistributed storage system 106 for storage and/or processing of big datasets. As used herein, big data may refer to partially or fullystructured, semi-structured, or unstructured data sets includingmillions of rows and hundreds of thousands of columns. A big data setmay be compiled, for example, from a history of purchase transactionsover time, from web registrations, from social media, from records ofcharge (ROC), from summaries of charges (SOC), from internal data,transaction network internal data, third party data, credit reportingbureau data, or from other suitable sources. Big data sets may becompiled without descriptive metadata such as column types, counts,percentiles, or other interpretive-aid data points.

In various embodiments, distributed storage system 106 may comprise oneor more nodes 108. Nodes 108 may comprise computers or processors thesame as or similar to lightning KNN host 102. Nodes 108 may bedistributed geographically in different locations, housed in the samebuilding, and/or housed in the same rack. Nodes 108 may also beconfigured to function in concert to provide storage space and/orprocessing power greater than one of a node 108 might provide alone. Asa result, distributed storage system 106 may collect and/or store thedata 110. Data 110 may be collected by nodes 108 individually andcompiled or in concert and collated. Data 110 may further be compiledinto a data set and formatted for use in lightning KNN method 200 ofFIG. 3.

In various embodiments, data 110 may comprise a collection of dataincluding and/or originating from cardholder information, transactioninformation, account information, record of sales, account history,customer history, sensor data, machine log data, data storage system,public web data, and/or social media. Data 110 may be collected frommultiple sources and amalgamated into a big data structure such as afile, for example. In that regard, the data may be used as an input togenerate metadata describing the big data structure itself, as well asthe data stored in the structure.

The distributed storage system 106 may comprise a transaction network. Alightning KNN host 102 may comprise various modules and engines asdiscussed herein wherein data records within data 110 may be evaluatedwhereby the “nearest neighbor” record(s) may be identified. A nearestneighbor record comprises a record having a field with a value more likeanother specific record, than any other record. For instance, a firstrecord, a second record and a third record may exist, each with a field,for instance, transaction size, having a value, for instance, a dollaramount. If the first record contains a transaction size comprising $100,the second record contains a transaction size comprising $200 and thethird record contains a transaction size comprising $1000, the firstrecord comprises a nearest neighbor record of the second record and thesecond record comprises a nearest neighbor record of the first record.Furthermore, the third record may be said to be a second nearestneighbor record of the second record, and a third nearest neighborrecord of the first record, in other words, the nearest neighbor recordsmay be ranked by proximity. As will be discussed further herein, alightning KNN method 200 may determine all the nearest neighbor recordsof all records within data 110 and/or a subset of data 110.

A lightning KNN method may thus be implemented in real-timeapplications. For instance, nearest neighbor analysis may be performedin real time or near real time by virtue of the quick computationalspeeds enjoyed by the method. Moreover, a lightning KNN method may beimplemented to perform model development for different segments of themarkets by identifying the nearest neighbors. Lightning KNN will permit,by virtue of the quick computational speeds enjoyed, the data setsevaluated to be quite large, thereby improving the accuracy of developedmodels. For instance, while traditional KNN methods may take as long as70 ms to determine an optimal credit line for a new cardholder,lightning KNN can in various embodiments determine an optimal creditline for a new cardholder in about 7 ms. In further embodiments,transaction data analysis on about ten billion transactions per year mayin traditional KNN require 8 hours of processing time, whereas lightningKNN can in various embodiments process such data in about 30 ms. Infurther embodiments, the speed enhancement may be leveraged to improveaccuracy and precision of results, such as allowing processing of morerecords per second. Lightning KNN will permit fraud detection, forinstance, facilitating the analysis of each transaction of each customerof a transaction card provider and in real time, whereby fraudulenttransactions may be more readily identified. As such, a lightning KNNmethod may be implemented for various objectives, for instance, byassessing records to determine nearest neighbors, a lightning KNN methodmay be implemented to determine the optimal line (e.g., the mostprofitable credit limit) for a new cardholder, such as by assessing theprofitability of every other similarly situated cardholder in view oftheir credit limits and picking the credit limit most frequentlyassociated with improved profitability.

In various embodiments, a lightning KNN method involves multiple complexand interactive machine steps. For instance, evaluating the data 110 ata transaction level provides sufficient granularity. Data may beevaluated at the transaction level and/or aggregated such as tofacilitate further data processing.

For instance, with reference to FIGS. 2-4F, a global new account engine10 may request that a global decisioning engine 20 determine an optimalline (e.g., the credit limit) that for a given new cardholder wouldresult in the most profit for the card issuer. The global decisioningengine 20 may pass data 110 to a real-time processing module 30. Thereal-time processing module 30 may comprise all or part of a lightningKNN host 102 configured to perform a lightning KNN method 200, and/ormay interact with a data warehouse module 40 to retrieve historicallyprocessed data 55 fully or partially processed according to a lightningKNN method 200, and may provide an optimal line recommendation 50 to theglobal decisioning engine 20. In various embodiments, the data warehousemodule 40 may comprise a data warehouse associated with batchprocessing, which in various embodiments may be referred to as“Cornerstone.”

In various embodiments, the lightning KNN method 200 may identify thenearest neighbor records, as discussed, wherein each record represents acardholder. Thus, “look alike” cardholders may be identified. The methodmay calculate the distance between the variables comprising the recordsfor every cardholder. This data may be stored as historically processeddata 55. Upon acquisition of a new cardholder, the method may identifythe three nearest neighbors, the first nearest neighbor, the secondnearest neighbor, and the third nearest neighbor, or any number ofnearest neighbors by assessing the historically processed data 55.

In various embodiments, sets of cardholders all located within a givendistance of one another (e.g., a “first cluster proximity”) may begrouped into sets (e.g., “a first cluster”). The records of eachcardholder may have fields and each field may have a value. The valuesof each cardholder within the first cluster may be ingested and a centervalue determined of the first cluster, for instance, an average of eachvalue of each cardholder. The center value may comprise a “first clustervalue,” and a corresponding cluster record may be created comprising acluster field having the first cluster value and be stored in thehistorically processed data 55.

In various embodiments, the lookalike cardholders of a new cardholdermay be desired to be identified. Rather than determining a distancebetween the variables comprising the new cardholder and every othercardholder, instead, the value of a variable of the new cardholder maybe compared to the first cluster value and the cardholder determined tobe proximate to the first cluster value. In various embodiments, theremay be multiple cluster values, for instance, a first cluster value, asecond cluster value and a third cluster value, as there may be multipleclusters, such as a first cluster, a second cluster and a third cluster.The nearest neighbor cluster of the new cardholder may be determined. Invarious embodiments, the lookalike cardholders of the new cardholder maybe desired to be identified, and/or a distance between the variablescomprising the new cardholder and other cardholders may be desired to beidentified. Because the nearest neighbor cluster of the new cardholderis determined, processing efficiency may be improved by only determiningthe look alike cardholders from within the nearest neighbor cluster ofthe new cardholder, or in various embodiments, form within the nearestneighbor cluster of the new cardholder, and only those other clusterswhose cluster value is no farther from the new cardholder than thenearest neighbor cluster and/or only those clusters of which a portionlies within a radius extending from the new cardholder and outward onlyso far as to include a first ceiling number (FCN) of records asdiscussed further herein.

More specifically, a lightning KNN method 200 may include determiningthe value of the field of the record 201 for all records (first record201-1, second record 201-2, third record 201-3, fourth record 201-4, Nthrecord 201-n) (FIG. 4A) (step 2001). Each of the records 201 eachrepresent a cardholder. For instance, a first record 201-1 represents afirst cardholder, a second record 201-2 represents a second cardholder,a third record 201-3 represents a third cardholder, a fourth record201-4 represents a fourth cardholder, and a Nth record 201-n representsa Nth cardholder. The records may be grouped into sets 301 locatedwithin a distance (e.g., cluster proximity 302) of a cluster value 303(step 2003). A cluster value 303 may comprise a center point of each set301. For instance, with reference to FIG. 4B, a first set 301-1 may havea first cluster proximity 302-1 comprising a radius about a firstcluster value 303-1. A second set 301-2 may have a second clusterproximity 302-2 comprising a radius about a second cluster value 303-2.A third set 301-3 may have a third cluster proximity 302-3 comprising aradius about a third cluster value 303-3. A fourth set 301-4 may have afourth cluster proximity 302-4 comprising a radius about a fourthcluster value 303-4. A fifth set 301-5 may have a fifth clusterproximity 302-5 comprising a radius about a fifth cluster value 303-5.Any number of sets 301 may have a cluster proximity 302 comprising aradius about a cluster value 303.

Each set 301 and associated cluster proximity 302 and cluster value 303may be stored in a historically processed data 55 database (FIG. 3)(step 2005). In this manner, offline processing may be leveraged tocompute cluster values 303 also known as K-Means values for sets ofrecords.

In various embodiments, it may be desired to evaluate no more than afirst ceiling number of records. For instance, evaluation of morerecords may result in slower processing. As such, a first ceiling number(FCN) of records may be set.

Upon acquisition of a new cardholder 310, the historically processeddata 55 may be loaded (step 2007). The lightning KNN method 200 mayinclude evaluating the distance between the cluster value 303 of eachset 301 and the corresponding value of the field of the recordcomprising the new cardholder 310 (step 2009). For instance, for a firstthrough fifth set 301-1 through 301-5, a first through fifthnew-cardholder-to-cluster-value distance 311-1 through 311-5 may bedetermined. As such, a plurality of new-cardholder-to-cluster valuedistances 311 may be determined.

For each of the first through fifth new-cardholder-to-cluster-valuedistances 311-1 through 311-5, the cluster proximity 302 of theassociated set 301 may be added to the first through fifthnew-cardholder-to-cluster-value distance 311-1 through 311-5 to form anouter radius boundary 312-1 through 312-5 of each set 301-1 through301-5, meaning the distance from the new cardholder 310 to the farthestmost boundary of each set 301 (step 2011).

In further embodiments, a first ceiling number radius 314 is received(step 2013). The first ceiling number radius 314 is the distance frombetween the new cardholder 310 to the FCN^(th) record. In this manner, aradius may be determined that includes no more than a FCN of records.Within the first ceiling number radius 314, variousnew-cardholder-to-cluster-value distances 311 will exist, eachassociated with a set 301. Each set 301 with anew-cardholder-to-cluster-value distance locating a portion of the set301 within the FCN radius 314 is retained, and all other sets 301discarded. In this manner, it may be said that only those sets that fallwithin or touch the deterministic circle are retained (step 2015).

In this manner, the number of distances necessary to be calculated inorder to determine lookalike cardholders may be diminished. Consequentlythe processing time may be improved.

The lightning KNN method 200 may be performed by a lightning KNN host102 as discussed. More specifically, various aspects of the lightningKNN host 102 may perform various aspects of the lightning KNN method200. In this manner, the lightning KNN host 102 may map a cardholderaccording to a lightning KNN method 200. For instance, a lightning KNNhost 102 may comprise a field value determiner 501. The field valuedeterminer may comprise a module configured to determine the value ofthe field of the record 201 as per step 2001. The lightning KNN host 102may comprise a record grouper 503. The record grouper may comprise amodule configured to group the records into sets as per step 2003. Thelightning KNN host 102 may comprise a historical data storage engine505. For instance, a historical data storage engine may be configured tostore each set, associated cluster proximity, and associated clustervalue in a historically processed data database as per step 2005. Thelightning KNN host 102 may comprise a historical data retrieval engine507. The historical data retrieval engine 507 may be configured to loadthe historically processed data per step 2007. The lightning KNN host102 may comprise a distance evaluator 509. The distance evaluator mayevaluate the distance between the cluster value of each set and thecorresponding value of the field of the record comprising the newcardholder, whereby a plurality of new-cardholder-to-cluster-valuedistances may be determined such as in step 2009. Furthermore, thelightning KNN host 102 may comprise an outer radius boundary determiner511. The outer radius boundary determiner 511 may determine the outerradius boundary of each set such as in step 511. Moreover, the lightningKNN host 102 may comprise a first ceiling number radius receiver 513configured to receive a first ceiling number radius. Finally, thelightning KNN host 102 my comprise a set discarder 515 configured toretain each set with a new-cardholder-to-cluster-value distance locatinga portion of the set within the first ceiling number radius such as instep 2015.

Each of these aspects of the lightning KNN host 102 may be in logicalcommunication with a lightning KNN communication bus 517. As such, eachsuch aspect may interoperate via lightning KNN communication bus 517 bytransceiving messages and data, and may perform various calculations,decisions, and operations in accordance with the teachings herein.Moreover, lightning KNN host 102 may further comprise a bus controller519 configured to manage communications among modules on the lightningKNN communication bus 517, and direct various modules to perform variousoperations and processes in accordance with methods disclosed herein, aswell as direct communications with external components such asdistributed storage system 106, nodes 108, and/or the like.

Data, as discussed herein, may include “internal data.” Internal datamay include any data a credit issuer possesses or acquires pertaining toa particular consumer. Internal data may be gathered before, during, orafter a relationship between the credit issuer and the transactionaccount holder (e.g., the consumer or buyer). Such data may includeconsumer demographic data. Consumer demographic data includes any datapertaining to a consumer. Consumer demographic data may include consumername, address, telephone number, email address, employer and socialsecurity number. Consumer transactional data is any data pertaining tothe particular transactions in which a consumer engages during any giventime period. Consumer transactional data may include, for example,transaction amount, transaction time, transaction vendor/merchant, andtransaction vendor/merchant location. Transaction vendor/merchantlocation may contain a high degree of specificity to a vendor/merchant.For example, transaction vendor/merchant location may include aparticular gasoline filing station in a particular postal code locatedat a particular cross section or address. Also, for example, transactionvendor/merchant location may include a particular web address, such as aUniform Resource Locator (“URL”), an email address and/or an InternetProtocol (“IP”) address for a vendor/merchant. Transactionvendor/merchant and transaction vendor/merchant location may beassociated with a particular consumer and further associated with setsof consumers. Consumer payment data includes any data pertaining to aconsumer's history of paying debt obligations. Consumer payment data mayinclude consumer payment dates, payment amounts, balance amount, andcredit limit. Internal data may further comprise records of consumerservice calls, complaints, requests for credit line increases,questions, and comments. A record of a consumer service call includes,for example, date of call, reason for call, and any transcript orsummary of the actual call.

Any communication, transmission and/or channel discussed herein mayinclude any system or method for delivering content (e.g. data,information, metadata, etc.), and/or the content itself. The content maybe presented in any form or medium, and in various embodiments, thecontent may be delivered electronically and/or capable of beingpresented electronically. For example, a channel may comprise a websiteor device (e.g., Facebook, YouTube®, AppleTV®, Pandora®, xBox®, Sony®Playstation®), a uniform resource locator (“URL”), a document (e.g., aMicrosoft Word® document, a Microsoft Excel® document, an Adobe .pdfdocument, etc.), an “ebook,” an “emagazine,” an application ormicroapplication (as described herein), an SMS or other type of textmessage, an email, Facebook, twitter, MMS and/or other type ofcommunication technology. In various embodiments, a channel may behosted or provided by a data partner. In various embodiments, thedistribution channel may comprise at least one of a merchant website, asocial media website, affiliate or partner websites, an external vendor,a mobile device communication, social media network and/or locationbased service. Distribution channels may include at least one of amerchant website, a social media site, affiliate or partner websites, anexternal vendor, and a mobile device communication. Examples of socialmedia sites include Facebook®, foursquare®, Twitter®, MySpace®,LinkedIn®, and the like. Examples of affiliate or partner websitesinclude American Express®, Groupon®, LivingSocial®, and the like.Moreover, examples of mobile device communications include texting,email, and mobile applications for smartphones.

A “consumer profile,” “customer data,” or “consumer profile data” maycomprise any information or data about a consumer that describes anattribute associated with the consumer (e.g., a preference, an interest,demographic information, personally identifying information, and thelike).

In various embodiments, the methods described herein are implementedusing the various particular machines described herein. The methodsdescribed herein may be implemented using the below particular machines,and those hereinafter developed, in any suitable combination, as wouldbe appreciated immediately by one skilled in the art. Further, as isunambiguous from this disclosure, the methods described herein mayresult in various transformations of certain articles.

For the sake of brevity, conventional data networking, applicationdevelopment and other functional aspects of the systems (and componentsof the individual operating components of the systems) may not bedescribed in detail herein. Furthermore, the connecting lines shown inthe various figures contained herein are intended to represent exemplaryfunctional relationships and/or physical couplings between the variouselements. It should be noted that many alternative or additionalfunctional relationships or physical connections may be present in apractical system.

The various system components discussed herein may include one or moreof the following: a host server or other computing systems including aprocessor for processing digital data; a memory coupled to the processorfor storing digital data; an input digitizer coupled to the processorfor inputting digital data; an application program stored in the memoryand accessible by the processor for directing processing of digital databy the processor; a display device coupled to the processor and memoryfor displaying information derived from digital data processed by theprocessor; and a plurality of databases. Various databases used hereinmay include: client data; merchant data; financial institution data;and/or like data useful in the operation of the system. As those skilledin the art will appreciate, user computer may include an operatingsystem (e.g., Windows NT®, Windows 95/98/2000®, Windows XP®, WindowsVista®, Windows 7®, OS2, UNIX®, Linux®, Solaris®, MacOS, etc.) as wellas various conventional support software and drivers typicallyassociated with computers.

The present system or any part(s) or function(s) thereof may beimplemented using hardware, software or a combination thereof and may beimplemented in one or more computer systems or other processing systems.However, the manipulations performed by embodiments were often referredto in terms, such as matching or selecting, which are commonlyassociated with mental operations performed by a human operator. No suchcapability of a human operator is necessary, or desirable in most cases,in any of the operations described herein. Rather, the operations may bemachine operations. Useful machines for performing the variousembodiments include general purpose digital computers or similardevices.

In fact, in various embodiments, the embodiments are directed toward oneor more computer systems capable of carrying out the functionalitydescribed herein. The computer system includes one or more processors,such as processor. The processor is connected to a communicationinfrastructure (e.g., a communications bus, cross over bar, or network).Various software embodiments are described in terms of this exemplarycomputer system. After reading this description, it will become apparentto a person skilled in the relevant art(s) how to implement variousembodiments using other computer systems and/or architectures. Computersystem can include a display interface that forwards graphics, text, andother data from the communication infrastructure (or from a frame buffernot shown) for display on a display unit.

Computer system also includes a main memory, such as for example randomaccess memory (RAM), and may also include a secondary memory. Thesecondary memory may include, for example, a hard disk drive and/or aremovable storage drive, representing a floppy disk drive, a magnetictape drive, an optical disk drive, etc. The removable storage drivereads from and/or writes to a removable storage unit in a well-knownmanner. Removable storage unit represents a floppy disk, magnetic tape,optical disk, etc. which is read by and written to by removable storagedrive. As will be appreciated, the removable storage unit includes acomputer usable storage medium having stored therein computer softwareand/or data.

In various embodiments, secondary memory may include other similardevices for allowing computer programs or other instructions to beloaded into computer system. Such devices may include, for example, aremovable storage unit and an interface. Examples of such may include aprogram cartridge and cartridge interface (such as that found in videogame devices), a removable memory chip (such as an erasable programmableread only memory (EPROM), or programmable read only memory (PROM)) andassociated socket, and other removable storage units and interfaces,which allow software and data to be transferred from the removablestorage unit to computer system.

Computer system may also include a communications interface.Communications interface allows software and data to be transferredbetween computer system and external devices. Examples of communicationsinterface may include a modem, a network interface (such as an Ethernetcard), a communications port, a Personal Computer Memory CardInternational Association (PCMCIA) slot and card, etc. Software and datatransferred via communications interface are in the form of signalswhich may be electronic, electromagnetic, and optical or other signalscapable of being received by communications interface. These signals areprovided to communications interface via a communications path (e.g.,channel). This channel carries signals and may be implemented usingwire, cable, fiber optics, a telephone line, a cellular link, a radiofrequency (RF) link, wireless and other communications channels.

The terms “computer program medium” and “computer usable medium” and“computer readable medium” are used to generally refer to media such asremovable storage drive and a hard disk installed in hard disk drive.These computer program products provide software to computer system.

Computer programs (also referred to as computer control logic) arestored in main memory and/or secondary memory. Computer programs mayalso be received via communications interface. Such computer programs,when executed, enable the computer system to perform the features asdiscussed herein. In particular, the computer programs, when executed,enable the processor to perform the features of various embodiments.Accordingly, such computer programs represent controllers of thecomputer system.

In various embodiments, software may be stored in a computer programproduct and loaded into computer system using removable storage drive,hard disk drive or communications interface. The control logic(software), when executed by the processor, causes the processor toperform the functions of various embodiments as described herein. Invarious embodiments, hardware components such as application specificintegrated circuits (ASICs). Implementation of the hardware statemachine so as to perform the functions described herein will be apparentto persons skilled in the relevant art(s).

The various system components may be independently, separately orcollectively suitably coupled to the network via data links whichincludes, for example, a connection to an Internet Service Provider(ISP) over the local loop as is typically used in connection withstandard modem communication, cable modem, Dish Networks®, ISDN, DigitalSubscriber Line (DSL), or various wireless communication methods, see,e.g., GILBERT HELD, UNDERSTANDING DATA COMMUNICATIONS (1996), which ishereby incorporated by reference. It is noted that the network may beimplemented as other types of networks, such as an interactivetelevision (ITV) network. Moreover, the system contemplates the use,sale or distribution of any goods, services or information over anynetwork having similar functionality described herein.

“Cloud” or “Cloud computing” includes a model for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, servers, storage, applications, and services)that can be rapidly provisioned and released with minimal managementeffort or service provider interaction. Cloud computing may includelocation-independent computing, whereby shared servers provideresources, software, and data to computers and other devices on demand.For more information regarding cloud computing, see the NIST's (NationalInstitute of Standards and Technology) definition of cloud computing athttp://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf (lastvisited June 2012), which is hereby incorporated by reference in itsentirety.

As used herein, “transmit” may include sending electronic data from onesystem component to another over a network connection. Additionally, asused herein, “data” may include encompassing information such ascommands, queries, files, data for storage, and the like in digital orany other form.

The computers discussed herein may provide a suitable website or otherInternet-based graphical user interface which is accessible by users. Inone embodiment, the Microsoft Internet Information Server (IIS),Microsoft Transaction Server (MTS), and Microsoft SQL Server, are usedin conjunction with the Microsoft operating system, Microsoft NT webserver software, a Microsoft SQL Server database system, and a MicrosoftCommerce Server. Additionally, components such as Access or MicrosoftSQL Server, Oracle, Sybase, Informix MySQL, Interbase, etc., may be usedto provide an Active Data Object (ADO) compliant database managementsystem. In one embodiment, the Apache web server is used in conjunctionwith a Linux operating system, a MySQL database, and the Perl, PHP,and/or Python programming languages.

Any of the communications, inputs, storage, databases or displaysdiscussed herein may be facilitated through a website having web pages.The term “web page” as it is used herein is not meant to limit the typeof documents and applications that might be used to interact with theuser. For example, a typical website might include, in addition tostandard HTML documents, various forms, Java applets, JavaScript, activeserver pages (ASP), common gateway interface scripts (CGI), extensiblemarkup language (XML), dynamic HTML, cascading style sheets (CSS), AJAX(Asynchronous Javascript And XML), helper applications, plug-ins, andthe like. A server may include a web service that receives a requestfrom a web server, the request including a URL(http://yahoo.com/stockquotes/ge) and an IP address (123.56.789.234).The web server retrieves the appropriate web pages and sends the data orapplications for the web pages to the IP address. Web services areapplications that are capable of interacting with other applicationsover a communications means, such as the internet. Web services aretypically based on standards or protocols such as XML, SOAP, AJAX, WSDLand UDDI. Web services methods are well known in the art, and arecovered in many standard texts. See, e.g., ALEX NGHIEM, IT WEB SERVICES:A ROADMAP FOR THE ENTERPRISE (2003), hereby incorporated by reference.

Practitioners will also appreciate that there are a number of methodsfor displaying data within a browser-based document. Data may berepresented as standard text or within a fixed list, scrollable list,drop-down list, editable text field, fixed text field, pop-up window,and the like. Likewise, there are a number of methods available formodifying data in a web page such as, for example, free text entry usinga keyboard, selection of menu items, check boxes, option boxes, and thelike.

The system and method may be described herein in terms of functionalblock components, screen shots, optional selections and variousprocessing steps. It should be appreciated that such functional blocksmay be realized by any number of hardware and/or software componentsconfigured to perform the specified functions. For example, the systemmay employ various integrated circuit components, e.g., memory elements,processing elements, logic elements, look-up tables, and the like, whichmay carry out a variety of functions under the control of one or moremicroprocessors or other control devices. Similarly, the softwareelements of the system may be implemented with any programming orscripting language such as C, C++, C#, Java, JavaScript, VBScript,Macromedia Cold Fusion, COBOL, Microsoft Active Server Pages, assembly,PERL, PHP, awk, Python, Visual Basic, SQL Stored Procedures, PL/SQL, anyUNIX shell script, and extensible markup language (XML) with the variousalgorithms being implemented with any combination of data structures,objects, processes, routines or other programming elements. Further, itshould be noted that the system may employ any number of conventionaltechniques for data transmission, signaling, data processing, networkcontrol, and the like. Still further, the system could be used to detector prevent security issues with a client-side scripting language, suchas JavaScript, VBScript or the like. For a basic introduction ofcryptography and network security, see any of the following references:(1) “Applied Cryptography: Protocols, Algorithms, And Source Code In C,”by Bruce Schneier, published by John Wiley & Sons (second edition,1995); (2) “Java Cryptography” by Jonathan Knudson, published byO'Reilly & Associates (1998); (3) “Cryptography & Network Security:Principles & Practice” by William Stallings, published by Prentice Hall;all of which are hereby incorporated by reference.

As will be appreciated by one of ordinary skill in the art, the systemmay be embodied as a customization of an existing system, an add-onproduct, a processing apparatus executing upgraded software, astandalone system, a distributed system, a method, a data processingsystem, a device for data processing, and/or a computer program product.Accordingly, any portion of the system or a module may take the form ofa processing apparatus executing code, an internet based embodiment, anentirely hardware embodiment, or an embodiment combining aspects of theinternet, software and hardware. Furthermore, the system may take theform of a computer program product on a computer-readable storage mediumhaving computer-readable program code means embodied in the storagemedium. Any suitable computer-readable storage medium may be utilized,including hard disks, CD-ROM, optical storage devices, magnetic storagedevices, and/or the like.

The system and method is described herein with reference to screenshots, block diagrams and flowchart illustrations of methods, apparatus(e.g., systems), and computer program products according to variousembodiments. It will be understood that each functional block of theblock diagrams and the flowchart illustrations, and combinations offunctional blocks in the block diagrams and flowchart illustrations,respectively, can be implemented by computer program instructions.

These computer program instructions may be loaded onto a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructionsthat execute on the computer or other programmable data processingapparatus create means for implementing the functions specified in theflowchart block or blocks. These computer program instructions may alsobe stored in a computer-readable memory that can direct a computer orother programmable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function specified in the flowchart block or blocks.The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchartillustrations support combinations of means for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instruction means for performing the specified functions. Itwill also be understood that each functional block of the block diagramsand flowchart illustrations, and combinations of functional blocks inthe block diagrams and flowchart illustrations, can be implemented byeither special purpose hardware-based computer systems which perform thespecified functions or steps, or suitable combinations of specialpurpose hardware and computer instructions. Further, illustrations ofthe process flows and the descriptions thereof may make reference touser windows, webpages, websites, web forms, prompts, etc. Practitionerswill appreciate that the illustrated steps described herein may comprisein any number of configurations including the use of windows, webpages,web forms, popup windows, prompts and the like. It should be furtherappreciated that the multiple steps as illustrated and described may becombined into single webpages and/or windows but have been expanded forthe sake of simplicity. In other cases, steps illustrated and describedas single process steps may be separated into multiple webpages and/orwindows but have been combined for simplicity.

The term “non-transitory” is to be understood to remove only propagatingtransitory signals per se from the claim scope and does not relinquishrights to all standard computer-readable media that are not onlypropagating transitory signals per se. Stated another way, the meaningof the term “non-transitory computer-readable medium” and“non-transitory computer-readable storage medium” should be construed toexclude only those types of transitory computer-readable media whichwere found in In Re Nuijten to fall outside the scope of patentablesubject matter under 35 U.S.C. § 101.

Systems, methods and computer program products are provided. In thedetailed description herein, references to “various embodiments”, “oneembodiment”, “an embodiment”, “an example embodiment”, etc., indicatethat the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, it is submitted that it iswithin the knowledge of one skilled in the art to affect such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described. After reading the description, itwill be apparent to one skilled in the relevant art(s) how to implementthe disclosure in alternative embodiments.

Benefits, other advantages, and solutions to problems have beendescribed herein with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any elements that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as critical, required, or essentialfeatures or elements of the disclosure. The scope of the disclosure isaccordingly to be limited by nothing other than the appended claims, inwhich reference to an element in the singular is not intended to mean“one and only one” unless explicitly so stated, but rather “one ormore.” Moreover, where a phrase similar to ‘at least one of A, B, and C’or ‘at least one of A, B, or C’ is used in the claims or specification,it is intended that the phrase be interpreted to mean that A alone maybe present in an embodiment, B alone may be present in an embodiment, Calone may be present in an embodiment, or that any combination of theelements A, B and C may be present in a single embodiment; for example,A and B, A and C, B and C, or A and B and C. Although the disclosureincludes a method, it is contemplated that it may be embodied ascomputer program instructions on a tangible computer-readable carrier,such as a magnetic or optical memory or a magnetic or optical disk. Allstructural, chemical, and functional equivalents to the elements of theabove-described exemplary embodiments that are known to those ofordinary skill in the art are expressly incorporated herein by referenceand are intended to be encompassed by the present claims. Moreover, itis not necessary for a device or method to address each and everyproblem sought to be solved by the present disclosure, for it to beencompassed by the present claims.

Furthermore, no element, component, or method step in the presentdisclosure is intended to be dedicated to the public regardless ofwhether the element, component, or method step is explicitly recited inthe claims. No claim element herein is to be construed under theprovisions of 35 U.S.C. 112 (f) unless the element is expressly recitedusing the phrase “means for.” As used herein, the terms “comprises”,“comprising”, or any other variation thereof, are intended to cover anon-exclusive inclusion, such that a process, method, article, orapparatus that comprises a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus.

What is claimed is:
 1. A host comprising: a processor; and a tangible,non-transitory memory configured to communicate with the processor; thetangible, non-transitory memory having instructions stored thereon that,in response to execution by the processor, cause the processor toperform operations; a historical data retrieval engine configured toload historically processed data; a distance evaluator configured toevaluate a distance between a cluster value of each set ofaccountholders and a corresponding value of a field of a record of a newaccountholder, whereby a plurality of new-accountholder-to-cluster-valuedistances are determined; an outer radius boundary determiner configuredto determine an outer radius boundary of each of the set ofaccountholders; a first ceiling number radius receiver configured toreceive a first ceiling number radius; a set of accountholders discarderconfigured to retain each of the set of accountholders with anew-accountholder-to-cluster-value distance locating a portion of theset of accountholders within the first ceiling number radius; acommunication bus disposed in logical communication with the historicaldata retrieval engine, the distance evaluator, the outer radius boundarydeterminer, the first ceiling number radius receiver, and the set ofaccountholders discarder; a bus controller disposed in logicalcommunication with the communication bus and configured to directcommunication among the historical data retrieval engine, the distanceevaluator, the outer radius boundary determiner, and the first ceilingnumber radius receiver, and the set of accountholders discarder; and theprocessor configured to map the record according to a known nearestneighbor (KNN) method.
 2. The host according to claim 1, furthercomprising: a field value determiner configured to determine a value ofthe field of the record of a plurality of records; a record grouperconfigured to group the plurality of records into sets ofaccountholders; and a historical data storage engine configured to storeeach set of accountholders and an associated cluster proximity and anassociated cluster value in a historically processed data set ofaccountholders.
 3. The host according to claim 2, wherein each record ofthe plurality of records represents a accountholder.
 4. The hostaccording to claim 1, wherein the cluster value comprises a center valueof the set of accountholders.
 5. The host according to claim 1, whereinthe field of the record of the new accountholder comprises a creditlimit.
 6. The host according to claim 1, wherein thenew-accountholder-to-cluster-value distance comprises the distancebetween the cluster value and a value of the field of the record of thenew accountholder.
 7. The host according to claim 1, wherein the outerradius boundary of the set of accountholders comprises a sum of acluster proximity and the new-accountholder-to-cluster-value distance.8. The host according to claim 1, wherein the historically processeddata includes a first distance between records for a set ofaccountholders of accountholders.
 9. The host according to claim 1,wherein the historical data retrieval engine is configured to load thehistorically processed data from a node of a distributed storage system.10. The host according to claim 1, further comprising a historical datastorage engine configured to create a cluster proximity comprising thefirst distance between the set of accountholders of accountholders. 11.The host according to claim 1, further comprising a historical datastorage engine configured to determine an average of values in fields ofthe records associated with the set of accountholders of accountholdersin a cluster to create a cluster value for the cluster comprising theset of accountholders of accountholders.
 12. The host according to claim1, further comprising a record grouper configured to group the recordsinto the set of accountholders of accountholders located within thecluster proximity of the cluster value, wherein the cluster valuecomprises a center point of the set of accountholders of accountholdersand the cluster proximity comprises a radius about the cluster value.13. The host according to claim 1, further comprising a historical datastorage engine configured to store the set of accountholders ofaccountholders, the cluster proximity associated with the set ofaccountholders of accountholders and the cluster value.
 14. The hostaccording to claim 1, wherein the outer radius boundary determiner isconfigured to determine the outer radius boundary of each set ofaccountholders by adding the cluster proximity of each of the set ofaccountholders of accountholders to a new-accountholder-to-cluster-valuedistance of the set of accountholders of accountholders.
 15. The hostaccording to claim 1, wherein the outer radius boundary of the set ofaccountholders of accountholders comprises a third distance from the newaccountholder to a farthest most boundary of each of the set ofaccountholders of accountholders.
 16. The host according to claim 14,wherein the farthest most boundary is a farthest distance from thecluster value.
 17. The host according to claim 1, wherein the firstceiling number radius comprises a fourth distance from the newcardholder encircling a first ceiling number of records.
 18. The hostaccording to claim 1, wherein the set of accountholders discarder isfurther configured to discard each of the set of accountholders ofcardholders with the new-cardholder-to-cluster-value distance notlocating a portion of the set of accountholders of cardholders withinthe first ceiling number radius.